Gastric Stromal Tumors Research Articles

Endoscopic "calabash" ligation and resection for small gastric mesenchymal tumors.

Gastric mesenchymal tumors (GMT) are identified as soft tissue neoplasms that arise from mesenchymal stem cells within the gastrointestinal tract. GMT primarily encompass gastric stromal tumors (GST), gastric leiomyomas, and gastric schwannomas. Although most GMT are benign, there are still potential malignant changes, especially GST. Thus, early surgical intervention is the primary treatment for GMT. We have designed a simple endoscopic "calabash" ligation and resection (ECLR) procedure to treat GMT. Its efficacy and safety need to be compared with those of traditional endoscopic techniques, such as endoscopic submucosal excavation (ESE). To assess the safety and effectiveness of ECLR in managing small GMT (sGMT) with a maximum diameter ≤ 20 mm by comparing to ESE. This retrospective analysis involved patients who were hospitalized in our institution between November 2021 and March 2023, underwent endoscopic resection, and received a pathological diagnosis of GMT. Cases with a tumor diameter ≤ 20 mm were chosen and categorized into two cohorts: Study and control groups. The study group was composed of patients treated with ECLR, whereas the control group was composed of those treated with ESE. Data on general clinical characteristics (gender, age, tumor diameter, tumor growth direction, tumor pathological type, and risk grade), surgery-related information (complete tumor resection rate, operation duration, hospitalization duration, hospitalization cost, and surgical complications), and postoperative follow-up were collected for both groups. The aforementioned data were subsequently analyzed and compared. Five hundred and eighty-nine individuals were included, with 297 cases in the control group and 292 in the study group. After propensity score matching, the final analysis incorporated 260 subjects in each cohort. The findings indicated that the study group exhibited shorter operation duration and lowered medical expenses relative to the control group. Furthermore, the study group reported less postoperative abdominal pain and had a lower incidence of intraoperative perforation and postoperative electrocoagulation syndrome than the control group. There were no substantial variations observed in other parameters among the two cohorts. ECLR is a viable and effective approach for managing sGMT.

Development and validation of a machine-learning model for preoperative risk of gastric gastrointestinal stromal tumors

BackgroundGastrointestinal stromal tumors (GISTs) have malignant potential, and treatment varies according to risk. However, no specific protocols exist to preoperatively assess the malignant potential of gastric stromal tumors (gGISTs). This study aimed to use machine learning (ML) to develop and validate clinically relevant preoperative models to predict the malignant potential of gGISTs. MethodsWe screened patients diagnosed with gGISTs at the Affiliated Hospital of North Sichuan Medical College. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to identify risk factors. Subsequently, an ensemble of ML models was deployed to determine the optimal classifier. Additionally, we harnessed SHapley Additive exPlanations (SHAP) for tailored risk profiling. ResultsWe enrolled 318 patients with gGISTs. Utilizing LASSO regression and multifactorial logistic regression, we analyzed the training dataset, revealing that the presence of endoscopic ultrasound (EUS) high-risk features, tumor border clarity, tumor diameter, and monocyte-to-lymphocyte ratio (MLR) were significant predictors of high malignancy risk in gGIST. As determined by our ML approach, the logistic classification model demonstrated optimal performance, with an area under the receiver operating characteristic curve of 0.919 and 0.925 for the training and test sets, respectively. Furthermore, decision curve analysis substantiated the clinical relevance of the model. ConclusionHigh-risk EUS features, ill-defined tumor margins, larger tumor diameters, and elevated MLR independently predicted heightened malignant potential in gGIST. We developed logistic regression models based on these factors, which were further interpreted using the SHAP methodology. This analytical approach facilitated personalized therapeutic decision-making for diverse patient populations.

An ensemble machine learning model assists in the diagnosis of gastric ectopic pancreas and gastric stromal tumors

ObjectiveTo develop an ensemble machine learning (eML) model using multiphase computed tomography (MPCT) for distinguishing between gastric ectopic pancreas (GEP) and gastric stromal tumors (GIST) in lesions < 3 cm.MethodsIn this study, we retrospectively collected MPCT images from 138 patients between April 2017 and June 2023 across two centers. Cohort 1 comprised 94 patients divided into a training cohort and an internal validation cohort, while the 44 patients from Cohort 2 constituted the external validation cohort. Deep learning (DL) models were constructed based on the lesion region, and radiomics features were extracted to develop radiomics models, which were later integrated into the fusion model. Model performance was assessed through the analysis of the area under the receiver operating characteristic curve (AUROC). The diagnostic efficacy of the optimal model was compared with that of a radiologist. Additionally, the radiologist with the assistance of the eML model provides a secondary diagnosis, to assess the potential clinical value of the model.ResultsAfter evaluation using an external validation cohort, the radiomics model demonstrated the highest performance in the venous phase, achieving AUROC of 0.87. The DL model showed optimal performance in the non-contrast phase, with AUROC of 0.81. The eML achieved the best performance across all models, with AUROC of 0.90. The use of eML-assisted analysis resulted in a significant improvement in the junior radiologist’s accuracy, rising from 0.77 to 0.93 (p < 0.05). However, the senior radiologist’s accuracy, while improving from 0.86 to 0.95, did not exhibit a statistically significant difference.ConclusioneML model based on MPCT can effectively distinguish between GEPs and GISTs < 3 cm.Critical relevance statementThe multiphase CT-based fusion model, incorporating radiomics and DL technology, proves effective in distinguishing between GEP and gastric stromal tumors, serving as a valuable tool to enhance diagnoses and offering references for clinical decision-making.Key PointsNo studies yet differentiated these tumors via radiomics or DL.Radiomics and DL methodologies unveil potentially distinct phenotypes within lesions.Quantitative analysis on CT for GIST and ectopic pancreas.Ensemble learning aids accurate diagnoses, assisting treatment decisions.Graphical

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.

Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Complete pathological response confirmed by central hepatectomy for liver metastases from gastric stromal tumor after treatment with imatinib mesylate: a case report

Complete pathological response confirmed by central hepatectomy for liver metastases from gastric stromal tumor after treatment with imatinib mesylate: a case report

Comparing endoscopic ultrasonography and double contrast-enhanced ultrasonography in the preoperative diagnosis of gastric stromal tumor

BackgroundThis study was designed to perform a comparative analysis between endoscopic ultrasonography (EUS) and double contrast-enhanced ultrasonography (DCEUS) for the preoperative diagnosis of gastric stromal tumors (GSTs).MethodsA retrospective study was conducted involving 139 patients with histologically confirmed GSTs. All patients preoperatively underwent DCEUS and EUS. The pathology reports were treated as the baseline and were retrospectively compared with the findings of EUS and DCEUS.ResultsOf the 139 lesions, 120 and 113 were correctly identified by DCEUS and EUS, respectively, with an accuracy of 86.3% and 81.3%. The results revealed an insignificant difference between these two methods (p = 0.189).ConclusionsDCEUS can display not only the locations, sizes, shapes, borders, internal echoes, but also show the blood perfusion patterns of GSTs. It is a highly accurate, noninvasive, and convenient method to be used at the pre-treatment stage.

Biallelic inactivation of SDHA results in comorbidity of pediatric recurrent neuroblastoma and gastric stromal tumor

Biallelic inactivation of SDHA results in comorbidity of pediatric recurrent neuroblastoma and gastric stromal tumor

Differentiating gastric schwannoma from gastric stromal tumor (≤5 cm) by histogram analysis based on iodine-based material decomposition images: a preliminary study.

This study aims to investigate the value of histogram analysis based on iodine-based material decomposition (IMD) images obtained through dual-energy computed tomography (DECT) to differentiate gastric schwannoma (GS) from gastric stromal tumor (GST) (≤5 cm) preoperatively. From January 2015 to January 2023, 15 patients with GS and 30 patients with GST (≤5 cm) who underwent biphasic contrast-enhanced scans using DECT were enrolled in this study. For each tumor, we reconstructed IMD images at the arterial phase (AP) and venous phase (VP). Nine histogram parameters were automatically extracted and selected using MaZda software based on the IMD of AP and VP, respectively, including mean, 1st, 10th, 50th, 90th, and 99th percentile of the iodine concentration value (Perc.01, Perc.10, Perc.50, Perc.90, and Perc.99), variance, skewness, and kurtosis. The extracted IMD histogram parameters were compared using the Mann-Whitney U-test. The optimal IMD histogram parameters were selected using receiver operating characteristic (ROC) curves. Among the IMD histogram parameters of AP, the mean, Perc.50, Perc.90, Perc.99, variance, and skewness of the GS group were lower than that of the GST group (all P < 0.05). Among the IMD histogram parameters of VP, Perc.90, Perc.99, and the variance of the GS group was lower than those of the GST group (all P < 0.05). The ROC analysis showed that Perc.99 (AP) generated the best diagnostic performance with the area under the curve, sensitivity, and specificity being 0.960, 86.67%, and 93.33%, respectively, when using 71.00 as the optimal threshold. Histogram analysis based on IMD images obtained through DECT holds promise as a valuable tool for the preoperative distinction between GS and GST (≤5 cm).

The value of color Doppler ultrasonography combined with serum tumor markers in differential diagnosis of gastric stromal tumor and gastric cancer.

This study aimed to explore the value of color Doppler ultrasonography combined with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) in differential diagnosis of gastric stromal tumor (GST) and gastric cancer (GC). An analysis of the clinical data of 180 patients with clinically suspected gastric space occupying lesions. According to the postoperative pathological results, 180 suspected gastric space-occupying lesion patients were divided into GST group (n = 83) and GC group (n = 97). Color Doppler ultrasonography, serum tumor markers CEA and CA19-9 were compared. The research results showed that serum CEA and CA19-9 levels were lower in patients with GST group than those with GC group (both P < 0.001). With postoperative pathology as the gold standard, detection rates of GST and GC by combination of color Doppler ultrasound (CDUS), serum CEA, and CA19-9 were higher than those of each index alone (both P < 0.001). There was no difference between detection rates of GST and GC by combination of CDUS, serum CEA, and CA19-9 (P = 0.058). Color Doppler ultrasonography combined with serum tumor markers CEA and CA19-9 tests has a certain differential diagnostic value for GST and GC, which may provide a reliable reference basis for clinical diagnosis and treatment.

Machine learning-based predictive model for the differential diagnosis of ≤ 5 cm gastric stromal tumor and gastric schwannoma based on CT images

The clinical symptoms of ≤ 5 cm gastric stromal tumor (GST) and gastric schwannoma (GS) are similar, but the treatment regimens are different. This study explored the value of computed tomography (CT) combined with machine learning (ML) algorithms to find the best model to discriminate them. A total of 126 patients with GST ≤ 5 cm and 35 patients with GS ≤ 5 during 2013–2022 were included. CT imaging features included qualitative data (tumor location, growth pattern, lobulation, surface ulcer status, necrosis, calcification, and surrounding lymph nodes) and quantitative data [long diameter (LD); short diameter (SD); LD/SD ratio; degree of enhancement (DE); heterogeneous degree (HD)]. Patients were randomly divided into a training set (n = 112) and test set (n = 49) using 7:3 stratified sampling. The univariate and multivariate logistic regression analysis were used to identify independent risk factors. Five ML algorithms were used to build prediction models: Support Vector Machine, k-Nearest Neighbor, Random Forest, Extra Trees, and Extreme Gradient Boosting Machine. The analysis identified that HDv, lobulation, and tumor growth site were independent risk factors (P < 0.05). We should focus on these three imaging features of tumors, which are relatively easy to obtain. The area under the curve for the SVM, KNN, RF, ET, and XGBoost prediction models were, respectively, 0.790, 0.895, 0.978, 0.988, and 0.946 for the training set, and were, respectively, 0.848, 0.892, 0.887, 0.912, and 0.867 for the test set. The CT combined with ML algorithms generated predictive models to improve the differential diagnosis of ≤ 5 cm GST and GS which has important clinical practical value. The Extra Trees algorithm resulted in the optimal model.

Computed tomographic imaging features to differentiate gastric schwannomas from gastrointestinal stromal tumours: a matched case–control study

To investigate clinical data and computed tomographic (CT) imaging features in differentiating gastric schwannomas (GSs) from gastric stromal tumours (GISTs) in matched patients, 31 patients with GSs were matched with 62 patients with GISTs (1:2) in sex, age, and tumour site. The clinical and imaging data were analysed. A significant (P < 0.05) difference was found in the tumour margin, enhancement pattern, growth pattern, and LD values between the 31 patients with GSs and 62 matched patients with GISTs. The GS lesions were mostly (93.5%) well defined while only 61.3% GIST lesions were well defined.The GS lesions were significantly (P = 0.036) smaller than the GIST lesions, with the LD ranging 1.5–7.4 (mean 3.67 cm) cm for the GSs and 1.0–15.30 (mean 5.09) cm for GIST lesions. The GS lesions were more significantly (P = 0.001) homogeneously enhanced (83.9% vs. 41.9%) than the GIST lesions. The GS lesions were mainly of the mixed growth pattern both within and outside the gastric wall (74.2% vs. 22.6%, P < 0.05) compared with that of GISTs. No metastasis or invasion of adjacent organs was present in any of the GS lesions, however, 1.6% of GISTs experienced metastasis and 3.2% of GISTs presented with invasion of adjacent organs. Heterogeneous enhancement and mixed growth pattern were two significant (P < 0.05) independent factors for distinguishing GS from GIST lesions. In conclusion: GS and GIST lesions may have significantly different features for differentiation in lesion margin, heterogeneous enhancement, mixed growth pattern, and longest lesion diameter, especially heterogeneous enhancement and mixed growth pattern.

Intelligent identification system of gastric stromal tumors based on blood biopsy indicators

BackgroundThe most prevalent mesenchymal-derived gastrointestinal cancers are gastric stromal tumors (GSTs), which have the highest incidence (60–70%) of all gastrointestinal stromal tumors (GISTs). However, simple and effective diagnostic and screening methods for GST remain a great challenge at home and abroad. This study aimed to build a GST early warning system based on a combination of machine learning algorithms and routine blood, biochemical and tumour marker indicators.MethodsIn total, 697 complete samples were collected from four hospitals in Gansu Province, including 42 blood indicators from 318 pretreatment GST patients, 180 samples of gastric polyps and 199 healthy individuals. In this study, three algorithms, gradient boosting machine (GBM), random forest (RF), and logistic regression (LR), were chosen to build GST prediction models for comparison. The performance and stability of the models were evaluated using two different validation techniques: 5-fold cross-validation and external validation. The DeLong test assesses significant differences in AUC values by comparing different ROC curves, the variance and covariance of the AUC value.ResultsThe AUC values of both the GBM and RF models were higher than those of the LR model, and this difference was statistically significant (P < 0.05). The GBM model was considered to be the optimal model, as a larger area was enclosed by the ROC curve, and the axes indicated robust model classification performance according to the accepted model discriminant. Finally, the integration of 8 top-ranked blood indices was proven to be able to distinguish GST from gastric polyps and healthy people with sensitivity, specificity and area under the curve of 0.941, 0.807 and 0.951 for the cross-validation set, respectively.ConclusionThe GBM demonstrated powerful classification performance and was able to rapidly distinguish GST patients from gastric polyps and healthy individuals. This identification system not only provides an innovative strategy for the diagnosis of GST but also enables the exploration of hidden associations between blood parameters and GST for subsequent studies on the prevention and disease surveillance management of GST. The GST discrimination system is available online for free testing of doctors and high-risk groups at https://jzlyc.gsyy.cn/bear/mobile/index.html.

Multi-Slice CT Features Predict Pathological Risk Classification in Gastric Stromal Tumors Larger Than 2 cm: A Retrospective Study.

The Armed Forces Institute of Pathology (AFIP) had higher accuracy and reliability in prognostic assessment and treatment strategies for patients with gastric stromal tumors (GSTs). The AFIP classification is frequently used in clinical applications. But the risk classification is only available for patients who are previously untreated and received complete resection. We aimed to investigate the feasibility of multi-slice MSCT features of GSTs in predicting AFIP risk classification preoperatively. The clinical data and MSCT features of 424 patients with solitary GSTs were retrospectively reviewed. According to pathological AFIP risk criteria, 424 GSTs were divided into a low-risk group (n = 282), a moderate-risk group (n = 72), and a high-risk group (n = 70). The clinical data and MSCT features of GSTs were compared among the three groups. Those variables (p < 0.05) in the univariate analysis were included in the multivariate analysis. The nomogram was created using the rms package. We found significant differences in the tumor location, morphology, necrosis, ulceration, growth pattern, feeding artery, vascular-like enhancement, fat-positive signs around GSTs, CT value in the venous phase, CT value increment in the venous phase, longest diameter, and maximum short diameter (all p < 0.05). Two nomogram models were successfully constructed to predict the risk of GSTs. Low- vs. high-risk group: the independent risk factors of high-risk GSTs included the location, ulceration, and longest diameter. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.911 (95% CI: 0.872-0.951), and the sensitivity and specificity were 80.0% and 89.0%, respectively. Moderate- vs. high-risk group: the morphology, necrosis, and feeding artery were independent risk factors of a high risk of GSTs, with an AUC value of 0.826 (95% CI: 0.759-0.893), and the sensitivity and specificity were 85.7% and 70.8%, respectively. The MSCT features of GSTs and the nomogram model have great practical value in predicting pathological AFIP risk classification between high-risk and non-high-risk groups before surgery.

Correction: A loop-assisted inversion technique for easy removal of a gastric stromal tumor in the fundus.

Correction: A loop-assisted inversion technique for easy removal of a gastric stromal tumor in the fundus.

Advanced gastric adenocarcinoma misdiagnosed as gastric stromal tumor.

Advanced gastric adenocarcinoma misdiagnosed as gastric stromal tumor.

Endoscopic and laparoscopic resections for gastric stromal tumors which one is better?

Endoscopic and laparoscopic resections for gastric stromal tumors which one is better?

Clinical observation of laparoscopic seromuscular dissection in the treatment of gastric stromal tumors

Objective To investigate the safety and oncological prognosis of laparoscopic seromuscular dissection (LSD) in the treatment of gastric stromal tumors. Material and methods From June 2016 to July 2022, 67 patients with gastric stromal tumors underwent laparoscopic seromuscular dissection (LSD), and 58 patients underwent non-LSD surgery during the same period (52 patients underwent laparoscopic gastric wedge resection (LWR), two patients underwent proximal gastrectomy, three patients underwent total gastrectomy, one patient underwent distal gastrectomy and partial hepatectomy). Gastric stromal tumor patients were compared to compare the surgical results, tumor relapse rate, and survival rate of the two groups. Results The results of the two groups were compared. For gastric stromal tumors, especially gastric stromal tumors located at ‘difficult sites’, LSD can reduce the amount of bleeding and the number of cutting staplers used during the operation, reduce the incidence of postoperative complications, shorten the postoperative hospitalization time, reduce the hospitalization cost and improve the quality of life of patients without increasing the operation time. Conclusion Laparoscopic seromuscular dissection for gastric stromal tumors is safe and technically feasible in the hands of experienced laparoscopic surgeons.

P-317 CT radiomics for the preoperative prediction of molecular subtypes in gastric stromal tumor

P-317 CT radiomics for the preoperative prediction of molecular subtypes in gastric stromal tumor

Computed tomography features of gastric leiomyoma versus gastric stromal tumor: a case–control study with propensity score matching

To differentiate gastric leiomyomas (GLs) and gastric stromal tumors (GSTs) based on preoperative enhanced computed tomography characteristics. Twenty-six pathologically confirmed GLs were propensity score-matched to 26 GSTs in a 1:1 ratio based on sex, age, tumor site, and tumor size. Tumor shape and contour, mucosal ulceration, growth pattern, enhancement pattern and degree, longest diameter, and longest diameter/vertical diameter ratio were compared between the groups. Hemorrhage, calcification, peripheral invasion, and distant metastasis were also included in the regression analysis for differentiation of the two tumors. Mucosal ulceration was significantly more frequent in GSTs than GLs. The enhancement degree of GSTs was significantly higher than that of GLs in the arterial and portal venous phases. Using enhancement degrees of 18 HU and 23 HU in the arterial phase and venous phase as cutoff values, respectively, we found that an enhancement degree of <18 HU in the arterial phase was an independent influential factor for diagnosis of GLs. No significant differences were found in other morphological characteristics. GLs did not metastasize or invade adjacent tissues. A low enhancement degree in GLs is the most valuable quantitative feature for differentiating these two similar tumors.

Risk factors of perforation in gastric stromal tumors during endoscopic resection: a retrospective case-control study.

Perforation is a common complication during endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) associated with secondary infections, sepsis, hospitalization time and cost. However, the risk factors of perforation remain controversial. This study aimed to investigate the risk factors for perforation during ER of gGISTs. This retrospective case-control study included consecutive patients with gGISTs who underwent ER between June 2009 and November 2021 at the Nanjing Drum Tower Hospital. Univariate and multivariate analyses were performed to investigate the risk factors for perforation. Sensitivity analyses with propensity scoring (PS) were performed to evaluate the stability of the independent effects. In total, 422 patients with gGISTs were included. The following factors were associated with perforation during ER: in the non-intraluminal growth patterns (all confounders adjusted odds ratio [aOR]: 5.39, 95% CI 2.99-9.72, P < 0.001), in the gastric fundus (aOR 2.25, 95% CI 1.40-3.60, P = 0.007), sized ≥ 2cm (aOR 1.70, 95% CI 1.04-2.77, P = 0.035), in the lesser curvature (aOR 0.12, 95% CI 0.05-0.27, P < 0.001), and in the gastric cardia (aOR 0.13, 95% CI 0.04-0.50, P = 0.003). The PS analysis confirmed the stable independent effects of these identified risk factors. ERs of gGISTs in non-intraluminal growth patterns, in the gastric fundus, and with larger tumor size were independent risk factors for perforation. While tumors in the lesser curvature or gastric cardia were independent protective factor for perforation.